Surface Defect Detection for Die Castings Based on the Improved YOLOv5 Method

Abstract

This article proposes a novel method for surface defect recognition of die-casting parts based on deep learning YOLOv5 network model. Previous methods, such as based on machine learning and based on template matching, can only classify defect type, and the accuracy and generalization of them are limited. The novel surface defects recognition method based on YOLOv5 algorithm can classify surface defects of die castings and accurately locate their positions which is import in powder metallurgy. To train the casting surface defect detection method based on the YOLOv5 algorithm, the transfer learning is initialized and trained on the Microsoft COCO dataset, we expanded the dataset based on the cyclegan algorithm, and used the kmeans++ algorithm to initialize the anchor-box size. We set up many groups of experiments, and experimental results show that our proposed method performed better than the previous method in joint identification of surface defects, and it can achieve very high mean of average precision (mAP@.5 and mAP@.5:.95) with more than 95%.